Category: Analytics

A typical machine learning (ML) workflow involves processes such as data extraction, data preprocessing, feature engineering, model training and evaluation, and model deployment. As data changes over time, when you deploy models to production, you want your model to learn continually from the stream of data. This means supporting the model’s ability to autonomously learn […]

Many organizations use Amazon SageMaker for their machine learning (ML) requirements and source data from a data lake stored on Amazon Simple Storage Service (Amazon S3). The petabyte scale source data on Amazon S3 may not always be clean because data lakes ingest data from several source systems, such as like flat files, external feeds, […]

In an effort to drive customer service improvements, many companies record the phone conversations between their customers and call center representatives. These call recordings are typically stored as audio files and processed to uncover insights such as customer sentiment, product or service issues, and agent effectiveness. To provide an accurate analysis of these audio files, […]

Amazon SageMaker notebooks now support R out-of-the-box, without needing you to manually install R kernels on the instances. Also, the notebooks come pre-installed with the reticulate library, which offers an R interface for the Amazon SageMaker Python SDK and enables you to invoke Python modules from within an R script. You can easily run machine […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. Sometimes it’s hard to find the right words to describe what you’re looking for. As the adage goes, “A picture is worth a thousand words.” Often, it’s easier to show a physical example or image than to try to describe […]

In today’s world, social media has become a place where customers share their experiences with services that they consume. Every telecom provider wants to have the ability to understand their customer pain points as soon as possible and to do this carriers frequently establish a social media team within their NOC (network operation center). This […]

AWS is excited to announce the general availability of Amazon SageMaker integration in QuickSight. You can now integrate your own Amazon SageMaker ML models with QuickSight to analyze the augmented data and use it directly in your business intelligence dashboards. As a business analyst, data engineer, or data scientist, you can perform ML inference in […]

As technology is advancing, your business is collecting more and more data from different sources. After collecting so many data points, it is often challenging to find the right insights to help your business grow. Dashboards are great at visualizing your data, based upon how you built them, but not always great at finding hidden […]

July 2024: The solution in this blog post is now obsolete with the release of Amazon Lex V2. You’ve rolled out a conversational interface powered by Amazon Lex, with a goal of improving the user experience for your customers. Now you want to track how well it’s working. Are your customers finding it helpful? How are […]

Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant inspection grades and scores from AWS […]